Spiritual epidemiology ANALYSIS (KW first pass)

Last updated: 2019-04-19

This is Kara’s first pass at analyzing the Spiritual Epidemiology data (with data from 2019-03-08). (Note: PDF printed portrait, 90% zoom, default margins.)

Porosity

For Spiritual Epidemiology, the Porosity scale consisted of 16 basic items, and follow-up questions to a subset of these items. For each of the basic items, participants could respond by saying “it does not happen” (scored as 0), “it might happen” (scored as 0.5), or “it definitely happens” (scored as 1).

Overall scores

First, I’ll examine overall “scores” on this scale, taking into account only the basic items (not the follow-up questions). Scores could range from 0 (equivalent to saying “it does not happen” for all 16 items) to 16 (equivalent to saying “it definitely happens” for all 16 items).

From these plots, it is clear that while there was substantial variability across individuals within each site, participants’ average response also varied fairly dramatically across sites, with participants in China endorsing the fewest items, participants in the US and Thailand endorsing slightly more, and participants in Ghana and Vanuatu endorsing the most.

Comparing porosity scores across sites [model: lm(score ~ epi_ctry, d_por_scored)]

parameter	b	standard error	t	p	significant
(Intercept)	6.84	0.10	71.30	<0.001	*
epi_ctrynonUS_US	0.55	0.05	12.08	<0.001	*
epi_ctryGHVT_THCH	4.13	0.11	38.05	<0.001	*
epi_ctryGH_VT	0.03	0.15	0.21	0.833
epi_ctryTH_CH	0.91	0.16	5.67	<0.001	*

Comparing porosity responses across sites [model: lmer(response ~ epi_ctry + (1 | epi_subj) + (epi_ctry | question)]

parameter	b	standard error	df	t	p	significant
(Intercept)	0.57	0.04	21.35	15.75	<0.001	*
epi_ctrynonUS_US	0.06	0.01	26.30	6.66	<0.001	*
epi_ctryGHVT_THCH	0.37	0.04	24.73	10.31	<0.001	*
epi_ctryGH_VT	-0.09	0.03	30.69	-3.09	0.004	*
epi_ctryTH_CH	0.08	0.02	199.61	5.18	<0.001	*

I ran two kinds of statistical analyses of these results: One comparing these average scores across sites (top table), and the other taking into account variability across individual participants and across individual items in the scale (bottom table). Theoretically, these analyses should yield pretty similar results, so I would be especially confident in effects that are consistent across the two analyses and that seem obvious from the plots.

Both analyses suggest that participants in sites outside of the US generally had significantly higher Porosity scores than participants in the US (see “epi_ctrynonUS_US” rows)—but looking at the plots it is clear that this was driven by participants in Ghana and Vanuatu, and was not true, on average, among participants in Thailand or China. In line with this, both analyses suggest that participants in Ghana and Vanuatu had significantly higher Porosity scores than participants in Thailand and China—and that this difference was far greater than the difference between “the West vs. the rest” (see “epi_ctrynonUS_US” rows). Both analyses also suggest that participants in Thailand had significantly higher Porosity scores, on average, than participants in China (see “epi_ctryTH_CH” rows).

By charismatic status

Now I’ll break things down by participants’ charismatic status.

Ignoring unknown parameters: height, widthJoining, by = c("epi_ctry", "epi_charc")
Column `epi_charc` has different attributes on LHS and RHS of join

From these plots, it looks like the general pattern of site differences held both among non-charismatics and charismatics, but that especially in sites where Porosity scores were generally lower (the US, Thailand, and China), charismatics scored higher in Porosity than non-charismatics. (This makes sense!)

Comparing porosity scores across sites and charismatic status [model: lm(score ~ epi_ctry * epi_charc, d_por_scored %>% (...))]

parameter	b	standard error	t	p	significant
(Intercept)	6.34	0.10	62.29	<0.001	*
epi_ctrynonUS_US	0.56	0.05	11.75	<0.001	*
epi_ctryGHVT_THCH	4.47	0.12	38.58	<0.001	*
epi_ctryGH_VT	0.05	0.16	0.29	0.771
epi_ctryTH_CH	0.40	0.16	2.45	0.015	*
epi_charccharc_non	2.54	0.25	10.34	<0.001	*
epi_ctrynonUS_US:epi_charccharc_non	0.01	0.11	0.07	0.944
epi_ctryGHVT_THCH:epi_charccharc_non	-2.14	0.28	-7.50	<0.001	*
epi_ctryGH_VT:epi_charccharc_non	-0.06	0.29	-0.20	0.841
epi_ctryTH_CH:epi_charccharc_non	0.18	0.49	0.36	0.719

I had trouble running the second kind of analysis (taking into account variability across individual participants and across individual items in the scale), so I only ran the first (using sum scores rather than individual responses).

This analysis confirmed that all of the general observations discussed in the previous section hold within the non-charismatic samples. In addition, collapsing across sites, charismatic participants tended to have higher Porosity scores (see “epi_charccharc_non” row), and this difference was more pronounced in Thailand and China than in Ghana and Vanuatu (see “epi_ctryGHVT_THCH:epi_charccharc_non”).

By item

Next, I’ll examine responses to individual items on the Porosity scale, first focusing on the basic items and then turning to the follow-up questions and a few site-specific questions. For each item, participants could say “it does not happen” (scored as 0), “it might happen” (scored as 0.5), or “it definitely happens” (scored as 1).

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There is so much to unpack here! When I look at these items, a few things that I think about are:

• Which items tend to follow the overall pattern established by the “overall scores” presented in the previous section? (e.g., question #1, many others)
• Among items that follow the overall pattern, for which items is this pattern exaggerated? (e.g., questions #5, #12) For which items is this pattern attentuated? (e.g., quesitons #2, #3)
• Which items do not follow this overall trend? (e.g., question #16)
• Which items seem to pick out an experience that is uniquely salient/prevalent in a particular site? (e.g., questions #7, #16)
• Which items seem to pick out an experience that is uniquely salient/prevalent among charismatics? (e.g., questions #4, #5)

Note that I just picked out a few examples to illustrate what I mean, but there are certainly other interesting cases that might be worth thinking about more deeply - e.g., question #15, where US participants appear more like Ghanaian & ni-Van participants than Thai or Chinese participants).

Here are participants’ answers to the follow-up questions for a subset of those basic items:

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And here are participants’ answers to a few site-specific questions:

Prayer (spiritual experience Question #1)

For Spiritual Epidemiology, the “spiritual experience” portion of the epidemiology began with two questions intended to gauge participants’ prayer practice. Here are participants’ responses to these two questions:

All spiritual experiences (Questions #2-23)

For Spiritual Epidemiology, the Spiritual Experiences scale consisted of 22 basic items (34 basic items in Thailand), and follow-up questions to a subset of these items. For each of the basic items, participants could respond by saying “no” (scored as 0), “maybe” (scored as 0.5; this might have only been available in some sites?), or “yes” (scored as 1).

Note: In addition to these 22 items, there were two items included as part of this section on “spiritual experiences” that were intended to gauge participants’ prayer practice - see Prayer (spiritual experience Question #1), above.

Overall scores

First, I’ll examine overall “scores” on this scale, taking into account only the basic items (not the follow-up questions).

In Thailand, there were an additional 12 quesitons (about experiences of additional “beings”) that; I have treated this discrepancy across sites in two different ways below: (1) Consdering all beings and using proportions, rather than sums, as “scores” (equivalent to asking, for each particiant, what proportion of the items they were exposed to did they endorse?); and (2) Considering only the first 5 beings asked about in each site, such that all participants were asked the same number of questions.

Considering all “beings”

Using the first approach (considering all “beings,” with 12 more beings in Thailand than in any other site), scores could range from 0 (equivalent to saying “no” for all 22-34 items) to 1 (equivalent to saying “yes” for all 22-34 items).

From these plots, it is clear that while there was substantial variability across individuals within each site, participants’ average response also varied fairly dramatically across sites, with participants in China endorsing the fewest items, participants in the US and Thailand endorsing slightly more, and participants in Ghana and Vanuatu endorsing the most. This is the same pattern we observed for Porosity scores, above.

Comparing spiritual experience scores across sites [model: lm(score ~ epi_ctry, d_spex_base_q2to23_scored_propall)]

parameter	b	standard error	t	p	significant
(Intercept)	0.28	0.01	50.77	<0.001	*
epi_ctrynonUS_US	0.01	0.00	3.72	<0.001	*
epi_ctryGHVT_THCH	0.10	0.01	15.74	<0.001	*
epi_ctryGH_VT	-0.04	0.01	-4.59	<0.001	*
epi_ctryTH_CH	0.07	0.01	7.77	<0.001	*

Comparing spiritual experience responses across sites [model: lmer(response ~ epi_ctry + (1 | epi_subj) + (epi_ctry | question), d_spex_base_q2to23)]

parameter	b	standard error	df	t	p	significant
(Intercept)	0.24	0.02	49.85	9.83	<0.001	*
epi_ctrynonUS_US	0.00	0.01	34.43	0.75	0.46
epi_ctryGHVT_THCH	0.10	0.02	32.57	5.60	<0.001	*
epi_ctryGH_VT	-0.05	0.03	29.42	-1.90	0.067
epi_ctryTH_CH	0.06	0.02	39.20	3.06	0.004	*

Again, I ran two kinds of statistical analyses of these results: One comparing these scores across sites (top table), and the other taking into account variability across individual participants and across individual items in the scale (bottom table). Theoretically, these analyses should yield pretty similar results, so I would be especially confident in effects that are consistent across the two analyses and that seem obvious from the plots.

Both analyses suggest that participants in sites outside of the US generally had significantly higher Porosity scores than participants in Ghana and Vanuatu had significantly higher Porosity scores than participants in Thailand and China (see “epi_ctrynonUS_US” rows), and that participants in Thailand had significantly higher Porosity scores, on average, than participants in China (see “epi_ctryTH_CH” rows).

By charismatic status

Now I’ll break things down by participants’ charismatic status.

Ignoring unknown parameters: height, widthJoining, by = c("epi_ctry", "epi_charc")
Column `epi_charc` has different attributes on LHS and RHS of join

From these plots, it looks like the general pattern of site differences held both among non-charismatics and charismatics, but that charismatics scored higher in Spiritual Experience than non-charismatics, across the board. (This makes sense!)

Comparing spiritual experience scores across sites and charismatic status [model: lm(score ~ epi_ctry * epi_charc, d_spex_base_q2to23_scored_propall %>% (...))]

parameter	b	standard error	t	p	significant
(Intercept)	0.26	0.01	41.83	<0.001	*
epi_ctrynonUS_US	0.01	0.00	4.36	<0.001	*
epi_ctryGHVT_THCH	0.10	0.01	14.14	<0.001	*
epi_ctryGH_VT	-0.04	0.01	-4.38	<0.001	*
epi_ctryTH_CH	0.06	0.01	6.17	<0.001	*
epi_charccharc_non	0.10	0.01	6.93	<0.001	*
epi_ctrynonUS_US:epi_charccharc_non	-0.01	0.01	-2.11	0.035	*
epi_ctryGHVT_THCH:epi_charccharc_non	-0.03	0.02	-1.98	0.048	*
epi_ctryGH_VT:epi_charccharc_non	0.01	0.02	0.53	0.597
epi_ctryTH_CH:epi_charccharc_non	-0.01	0.03	-0.33	0.739

I didn’t try to run the second kind of analysis (taking into account variability across individual participants and across individual items in the scale), only the first (using sum scores rather than individual responses).

This analysis confirmed that all of the general observations discussed in the previous section hold within the non-charismatic samples. In addition, collapsing across sites, charismatic participants tended to have higher Porosity scores (see “epi_charccharc_non” row), and this difference was more pronounced in non-US samples than in the US sample (see “r_spex_base_q2to23_scored_propall_bycharc” row), and more pronounced in Thailand and China than in Ghana and Vanuatu (see “epi_ctryGHVT_THCH:epi_charccharc_non”).

Considering first five “beings” only

Using the second approach (considering only the first five “beings” in each site), scores could range from 0 (equivalent to saying “no” for all 22 items) to 22 (equivalent to saying “yes” for all 22 items).

These plots look very similar to the plots considering all beings (which I find reassuring).

Comparing spiritual experience scores across sites [model: lm(score ~ epi_ctry, d_spex_base_q2to23_scored_first5)]

parameter	b	standard error	t	p	significant
(Intercept)	6.19	0.12	50.66	<0.001	*
epi_ctrynonUS_US	0.23	0.06	3.87	<0.001	*
epi_ctryGHVT_THCH	2.08	0.14	14.99	<0.001	*
epi_ctryGH_VT	-0.90	0.19	-4.83	<0.001	*
epi_ctryTH_CH	1.73	0.20	8.45	<0.001	*

This pattern of results are identical to the pattern of results considering all beings (which, again, I find reassuring).

By charismatic status

Now I’ll break things down by participants’ charismatic status.

Ignoring unknown parameters: height, widthJoining, by = c("epi_ctry", "epi_charc")
Column `epi_charc` has different attributes on LHS and RHS of join

Again, from these plots, it looks like the general pattern of site differences held both among non-charismatics and charismatics, but that charismatics scored higher in Spiritual Experience than non-charismatics, across the board. (This makes sense!)

Comparing spiritual experience scores across sites and charismatic status [model: lm(score ~ epi_ctry * epi_charc, d_spex_base_q2to23_scored_first5 %>% (...))]

parameter	b	standard error	t	p	significant
(Intercept)	5.71	0.14	41.75	<0.001	*
epi_ctrynonUS_US	0.29	0.06	4.47	<0.001	*
epi_ctryGHVT_THCH	2.10	0.16	13.46	<0.001	*
epi_ctryGH_VT	-0.99	0.22	-4.48	<0.001	*
epi_ctryTH_CH	1.49	0.22	6.83	<0.001	*
epi_charccharc_non	2.24	0.33	6.80	<0.001	*
epi_ctrynonUS_US:epi_charccharc_non	-0.30	0.14	-2.05	0.04	*
epi_ctryGHVT_THCH:epi_charccharc_non	-0.76	0.38	-1.98	0.048	*
epi_ctryGH_VT:epi_charccharc_non	0.14	0.39	0.35	0.724
epi_ctryTH_CH:epi_charccharc_non	-0.25	0.66	-0.38	0.707

I didn’t try to run the second kind of analysis (taking into account variability across individual participants and across individual items in the scale), only the first (using sum scores rather than individual responses).

This analysis confirmed that all of the general observations discussed in the previous section hold within the non-charismatic samples. In addition, collapsing across sites, charismatic participants tended to have higher Porosity scores (see “epi_charccharc_non” row), and this difference was more pronounced in non-US samples than in the US sample (see “r_spex_base_q2to23_scored_first5_bycharc” row), and more pronounced in Thailand and China than in Ghana and Vanuatu (see “epi_ctryGHVT_THCH:epi_charccharc_non”).

In the following four sections (Spiritual experiences: Universal questions, set 1 (Questions #2-10), Spiritual experiences: Beings questions (Questions #11-15 + more for Thailand), Spiritual experiences: Universal questions, set 2 (Questions #16-21), and Spiritual experiences: Universal questions, set 3 (Questions #22-23)), I look at scores for sub-sections of the Spiritual Experience scale, and at responses to individual items in this scale, including follow-up items. I do not provide text commentary on these additional plots and analyses at this time, but they all follow the same logic as the plots and analyses that I have annotated above.

Spiritual experiences: Universal questions, set 1 (Questions #2-10)

Overall scores

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Spiritual experiences: Beings questions (Questions #11-15 + more for Thailand)

Overall scores

Considering all “beings” (more for Thailand)

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Considering first five “beings” only

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Spiritual experiences: Universal questions, set 2 (Questions #16-21)

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Spiritual experiences: Universal questions, set 3 (Questions #22-23)

Overall scores

By item

Relationships between porosity and spiritual experience

Finally, I will consider the relationship between Porosity scores and Spiritual Experience scores, comparing this relationship across sites and examining this relationship within each site indivdidually. Again, I will explore two ways of handling the discrepancy between questions asked in Thailand vs. other sites.

Considering all “beings” (more for Thailand)

From the plot, it is clear that there is a general relationship between Porosity and Spiritual Experience scores. The strength of this relationships appears to vary somewhat across sites, but visual inspection suggests that this relationship is present to some degree in each site individually.

First, I’ll look at this relationship considering all sites in the same analysis (standardizing scores collapsing across all sites, to maintain the pattern fo general differences in Porosity and Spiritual Experience across sites):

Relationship between porosity and spiritual experience, comparing across sites [model: lm(por_score_std_collapse ~ spex_score_propall_std_collapse * epi_ctry, d_por_spex_base_q2to23_propall)]

parameter	b	standard error	t	p	significant
(Intercept)	0.03	0.02	1.40	0.161
spex_score_propall_std_collapse	0.40	0.02	18.95	<0.001	*
epi_ctrynonUS_US	0.10	0.01	12.15	<0.001	*
epi_ctryGHVT_THCH	0.64	0.02	27.05	<0.001	*
epi_ctryGH_VT	0.08	0.03	2.83	0.005	*
epi_ctryTH_CH	-0.04	0.04	-0.95	0.345
spex_score_propall_std_collapse:epi_ctrynonUS_US	-0.03	0.01	-2.84	0.005	*
spex_score_propall_std_collapse:epi_ctryGHVT_THCH	-0.15	0.02	-6.18	<0.001	*
spex_score_propall_std_collapse:epi_ctryGH_VT	-0.06	0.03	-2.51	0.012	*
spex_score_propall_std_collapse:epi_ctryTH_CH	-0.07	0.04	-1.74	0.083

Including all of the data in a single analysis suggests that, collapsing across sites, the relationship between Porosity and Spiritual Experience scores is significantly positive (see “spex_score_propall_std” row in the table above). However, this relationship appears to be significantly weaker among participants outside of the US than it is among US participants (see “spex_score_propall_std:epi_ctrynonUS_US” row), significantly weaker among participants in Ghana and Vanuatu relative to participants in Thailand and China (see “spex_score_propall_std:epi_ctryGHVT_THCH” row), and significantly weaker among participants in Ghana relative to participants in Vanuatu (see “spex_score_propall_std:epi_ctryGH_VT” row). In other words, the relationship between Porosity and Spiritual Experience seems to have been strongest in the US and Chinese samples, weaker in the ni-Van and Thai samples, and weakest in the Ghanaian sample. (This is true even after accounting for overall differences in porosity and spiritual experience across sites.)

Next, I’ll look at this relationship considering each sites individually in its own analysis (standardizing scores within each site):

Relationship between porosity and spiritual experience, within each site separately [model: lm(por_score_std ~ spex_score_propall_std, d_por_spex_base_q2to23_propall %>% filter(epi_ctry == ...)]

parameter	b	standard error	t	p	significant
US
(Intercept)	0.00	0.05	0.00	1
spex_score_propall_std	0.63	0.05	12.03	<0.001	*
Ghana
(Intercept)	0.00	0.06	0.00	1
spex_score_propall_std	0.29	0.06	4.75	<0.001	*
Thailand
(Intercept)	0.00	0.06	0.00	1
spex_score_propall_std	0.46	0.06	7.35	<0.001	*
China
(Intercept)	0.00	0.06	0.00	1
spex_score_propall_std	0.69	0.06	12.03	<0.001	*
Vanuatu
(Intercept)	0.00	0.06	0.00	1
spex_score_propall_std	0.49	0.06	7.75	<0.001	*

This analysis suggests that the relationship between Porosity and Spiritual Experience is signficantly positive in all five sites considered individually. The regression coefficients (“b” column in the table above) further confirm that the strength of this relationhip varied across sites: It was strongest in China and the US, middling in Vanuatu and Thailand, and weakest (but still significantly positive) in Ghana.

By charismatic status

From the plot, it looks like this relationship was similar across charismatic and non-charismatic participants (with the possible exception of Vanuatu, where it appears to be particularly strong among charismatics).

First, I’ll look at this relationship considering all sites in the same analysis (standardizing scores collapsing across all sites, to maintain the pattern fo general differences in Porosity and Spiritual Experience across sites):

Relationship between porosity and spiritual experience, comparing across sites and charismatic status [model: lm(por_score_std_collapse ~ spex_score_propall_std_collapse * epi_ctry * epi_charc, d_por_spex_base_q2to23_propall)]

parameter	b	standard error	t	p	significant
(Intercept)	-0.06	0.02	-2.63	0.009	*
spex_score_propall_std_collapse	0.32	0.02	13.31	<0.001	*
epi_ctrynonUS_US	0.09	0.01	9.26	<0.001	*
epi_ctryGHVT_THCH	0.74	0.03	28.68	<0.001	*
epi_ctryGH_VT	0.07	0.03	2.31	0.021	*
epi_ctryTH_CH	-0.11	0.04	-2.67	0.008	*
epi_charccharc_non	0.35	0.05	7.15	<0.001	*
spex_score_propall_std_collapse:epi_ctrynonUS_US	-0.05	0.01	-4.47	<0.001	*
spex_score_propall_std_collapse:epi_ctryGHVT_THCH	-0.06	0.03	-2.29	0.022	*
spex_score_propall_std_collapse:epi_ctryGH_VT	-0.05	0.03	-1.48	0.14
spex_score_propall_std_collapse:epi_ctryTH_CH	-0.15	0.05	-3.36	<0.001	*
spex_score_propall_std_collapse:epi_charccharc_non	0.16	0.06	2.74	0.006	*
epi_ctrynonUS_US:epi_charccharc_non	0.05	0.02	2.15	0.032	*
epi_ctryGHVT_THCH:epi_charccharc_non	-0.40	0.06	-7.11	<0.001	*
epi_ctryGH_VT:epi_charccharc_non	0.03	0.06	0.56	0.578
epi_ctryTH_CH:epi_charccharc_non	0.05	0.10	0.51	0.608
spex_score_propall_std_collapse:epi_ctrynonUS_US:epi_charccharc_non	0.07	0.03	2.73	0.006	*
spex_score_propall_std_collapse:epi_ctryGHVT_THCH:epi_charccharc_non	-0.19	0.07	-2.79	0.005	*
spex_score_propall_std_collapse:epi_ctryGH_VT:epi_charccharc_non	-0.05	0.05	-1.04	0.3
spex_score_propall_std_collapse:epi_ctryTH_CH:epi_charccharc_non	-0.26	0.13	-2.02	0.043	*

Among other things, this analysis suggests that the relationship between Porosity and Spiritual Experience is significantly positive among non-charismatics, collapsing across sites (see “spex_score_propall_std” row in the table above). In addition to varying across sites, the strength of this relationship also varied with charismatic status: It was particularly strong among charismatics relative to non-charismatic (see “spex_score_propall_std_collapse:epi_charccharc_non” row).

Next, I’ll look at this relationship considering each sites individually in its own analysis (standardizing scores within each site):

Relationship between porosity and spiritual experience, within each site separately [model: lm(por_score_std ~ spex_score_propall_std * epi_charc, d_por_spex_base_q2to23_propall %>% filter(epi_ctry == ...)]

parameter	b	standard error	t	p	significant
US
(Intercept)	-0.03	0.06	-0.59	0.559
spex_score_propall_std	0.62	0.06	10.24	<0.001	*
epi_charccharc_non	0.27	0.16	1.66	0.099
spex_score_propall_std:epi_charccharc_non	-0.15	0.15	-0.96	0.338
Ghana
(Intercept)	-0.01	0.08	-0.19	0.851
spex_score_propall_std	0.30	0.09	3.51	<0.001	*
epi_charccharc_non	0.05	0.13	0.35	0.73
spex_score_propall_std:epi_charccharc_non	-0.02	0.13	-0.17	0.862
Thailand
(Intercept)	-0.38	0.06	-6.40	<0.001	*
spex_score_propall_std	0.18	0.06	2.95	0.004	*
epi_charccharc_non	1.27	0.13	10.01	<0.001	*
spex_score_propall_std:epi_charccharc_non	0.17	0.12	1.40	0.163
China
(Intercept)	-0.07	0.05	-1.31	0.191
spex_score_propall_std	0.57	0.06	10.13	<0.001	*
epi_charccharc_non	0.29	0.27	1.08	0.281
spex_score_propall_std:epi_charccharc_non	0.80	0.20	3.99	<0.001	*
Vanuatu
(Intercept)	-0.03	0.07	-0.35	0.73
spex_score_propall_std	0.43	0.08	5.66	<0.001	*
epi_charccharc_non	0.04	0.14	0.26	0.798
spex_score_propall_std:epi_charccharc_non	0.16	0.13	1.16	0.246

This analysis suggests that the relationship between Porosity and Spiritual Experience is signficantly positive among non-charismatics in all five sites considered individually. The strength of this relationship only seems to vary by charismatic status in China (puzzling, not in Vanuatu, though that’s what it looked like from the plot).

Considering charismatics alone

Relationship between porosity and spiritual experience, comparing across sites, considering charismatics only [model: lm(por_score_std_collapse ~ spex_score_propall_std_collapse * epi_ctry, d_por_spex_base_q2to23_propall %>% filter(...))]

parameter	b	standard error	t	p	significant
(Intercept)	0.32	0.04	8.96	<0.001	*
spex_score_propall_std_collapse	0.30	0.04	7.49	<0.001	*
epi_ctrynonUS_US	0.19	0.02	8.68	<0.001	*
epi_ctryGHVT_TH	0.25	0.03	8.61	<0.001	*
epi_ctryGH_VT	0.11	0.05	2.17	0.031	*
spex_score_propall_std_collapse:epi_ctrynonUS_US	-0.03	0.03	-0.98	0.33
spex_score_propall_std_collapse:epi_ctryGHVT_TH	-0.03	0.04	-0.85	0.395
spex_score_propall_std_collapse:epi_ctryGH_VT	-0.10	0.04	-2.58	0.011	*

The relationship between porosity and spiritual experience is significantly positive among charismatics considered alone (excluding the 11 charismatics from China, since this is such a small sample). This relationship is stronger among charismatics in Vanuatu than among charismatics in Ghana.

Relationship between porosity and spiritual experience, within each site separately [model: lm(por_score_std ~ spex_score_propall_std, d_por_spex_base_q2to23_propall %>% filter(epi_ctry == ..., epi_charc == 'charismatic')]

parameter	b	standard error	t	p	significant
US
(Intercept)	0.23	0.13	1.72	0.092
spex_score_propall_std	0.48	0.13	3.78	<0.001	*
Ghana
(Intercept)	0.03	0.11	0.29	0.774
spex_score_propall_std	0.28	0.09	2.95	0.004	*
Thailand
(Intercept)	0.90	0.13	7.01	<0.001	*
spex_score_propall_std	0.35	0.12	3.04	0.004	*
Vanuatu
(Intercept)	0.01	0.09	0.11	0.916
spex_score_propall_std	0.59	0.09	6.74	<0.001	*

And it’s significantly positive in each sample of charismatics, considered alone (again, ignoring China).

By religiosity

What happens when we include self-reported religiosity in these models?

First, I’ll look at this relationship considering all sites in the same analysis (standardizing scores collapsing across all sites, to maintain the pattern fo general differences in Porosity and Spiritual Experience across sites):

Relationship between porosity and spiritual experience, comparing across sites and charismatic status, taking into account self-reported religiosity [model: lm(por_score_std_collapse ~ spex_score_propall_std_collapse * epi_ctry * epi_charc * epi_demo_howr_num, d_por_spex_base_q2to23_propall)]

parameter	b	standard error	t	p	significant
(Intercept)	-0.08	0.03	-2.72	0.007	*
spex_score_propall_std_collapse	0.25	0.03	7.42	<0.001	*
epi_ctrynonUS_US	0.08	0.01	7.49	<0.001	*
epi_ctryGHVT_THCH	0.71	0.03	20.49	<0.001	*
epi_ctryGH_VT	-0.03	0.05	-0.52	0.604
epi_ctryTH_CH	-0.13	0.05	-2.68	0.007	*
epi_charccharc_non	0.21	0.12	1.73	0.085
epi_demo_howr_num	0.12	0.03	3.69	<0.001	*
spex_score_propall_std_collapse:epi_ctrynonUS_US	-0.04	0.01	-2.85	0.004	*
spex_score_propall_std_collapse:epi_ctryGHVT_THCH	0.04	0.04	1.03	0.303
spex_score_propall_std_collapse:epi_ctryGH_VT	0.01	0.06	0.16	0.876
spex_score_propall_std_collapse:epi_ctryTH_CH	-0.18	0.06	-3.14	0.002	*
spex_score_propall_std_collapse:epi_charccharc_non	0.16	0.12	1.31	0.192
epi_ctrynonUS_US:epi_charccharc_non	0.04	0.04	0.88	0.381
epi_ctryGHVT_THCH:epi_charccharc_non	-0.27	0.15	-1.84	0.066
epi_ctryGH_VT:epi_charccharc_non	0.19	0.08	2.27	0.023	*
epi_ctryTH_CH:epi_charccharc_non	0.42	0.28	1.51	0.132
spex_score_propall_std_collapse:epi_demo_howr_num	-0.09	0.04	-2.35	0.019	*
epi_ctrynonUS_US:epi_demo_howr_num	-0.02	0.01	-1.31	0.19
epi_ctryGHVT_THCH:epi_demo_howr_num	0.01	0.04	0.13	0.898
epi_ctryGH_VT:epi_demo_howr_num	0.16	0.06	2.75	0.006	*
epi_ctryTH_CH:epi_demo_howr_num	-0.13	0.05	-2.38	0.018	*
epi_charccharc_non:epi_demo_howr_num	0.10	0.13	0.79	0.431
spex_score_propall_std_collapse:epi_ctrynonUS_US:epi_charccharc_non	-0.01	0.04	-0.29	0.771
spex_score_propall_std_collapse:epi_ctryGHVT_THCH:epi_charccharc_non	-0.25	0.15	-1.69	0.092
spex_score_propall_std_collapse:epi_ctryGH_VT:epi_charccharc_non	-0.05	0.09	-0.60	0.547
spex_score_propall_std_collapse:epi_ctryTH_CH:epi_charccharc_non	0.05	0.28	0.19	0.846
spex_score_propall_std_collapse:epi_ctrynonUS_US:epi_demo_howr_num	-0.02	0.01	-1.86	0.063
spex_score_propall_std_collapse:epi_ctryGHVT_THCH:epi_demo_howr_num	0.01	0.04	0.29	0.775
spex_score_propall_std_collapse:epi_ctryGH_VT:epi_demo_howr_num	-0.10	0.07	-1.55	0.121
spex_score_propall_std_collapse:epi_ctryTH_CH:epi_demo_howr_num	-0.15	0.06	-2.58	0.01	*
spex_score_propall_std_collapse:epi_charccharc_non:epi_demo_howr_num	0.20	0.14	1.40	0.163
epi_ctrynonUS_US:epi_charccharc_non:epi_demo_howr_num	0.04	0.04	0.98	0.328
epi_ctryGHVT_THCH:epi_charccharc_non:epi_demo_howr_num	-0.15	0.15	-0.98	0.327
epi_ctryGH_VT:epi_charccharc_non:epi_demo_howr_num	-0.24	0.10	-2.43	0.015	*
epi_ctryTH_CH:epi_charccharc_non:epi_demo_howr_num	-0.14	0.29	-0.47	0.635
spex_score_propall_std_collapse:epi_ctrynonUS_US:epi_charccharc_non:epi_demo_howr_num	0.13	0.05	2.68	0.008	*
spex_score_propall_std_collapse:epi_ctryGHVT_THCH:epi_charccharc_non:epi_demo_howr_num	-0.14	0.17	-0.79	0.428
spex_score_propall_std_collapse:epi_ctryGH_VT:epi_charccharc_non:epi_demo_howr_num	0.06	0.11	0.58	0.56
spex_score_propall_std_collapse:epi_ctryTH_CH:epi_charccharc_non:epi_demo_howr_num	-0.03	0.33	-0.09	0.925

Among other things, this analysis suggests that the relationship between Porosity and Spiritual Experience is significantly positive among non-charismatics, collapsing across sites, even after controlling for religiosity (see “spex_score_propall_std” row in the table above)—but it may have been weaker among more religious participants (see “spex_score_propall_std_collapse:epi_demo_howr_num” row).

Next, I’ll look at this relationship considering each sites individually in its own analysis (standardizing scores within each site):

Relationship between porosity and spiritual experience, accounting for religiosity, within each site separately [model: lm(por_score_std ~ spex_score_propall_std * epi_charc * epi_demo_how_num, d_por_spex_base_q2to23_propall %>% filter(epi_ctry == ...)]

parameter	b	standard error	t	p	significant
US
(Intercept)	-0.01	0.06	-0.22	0.824
spex_score_propall_std	0.49	0.07	7.25	<0.001	*
epi_charccharc_non	0.01	0.30	0.05	0.963
epi_demo_howr_num	0.29	0.07	4.31	<0.001	*
spex_score_propall_std:epi_charccharc_non	0.28	0.24	1.18	0.239
spex_score_propall_std:epi_demo_howr_num	0.01	0.07	0.10	0.918
epi_charccharc_non:epi_demo_howr_num	0.00	0.34	0.00	0.997
spex_score_propall_std:epi_charccharc_non:epi_demo_howr_num	-0.46	0.27	-1.68	0.095
Ghana
(Intercept)	0.00	0.08	0.03	0.972
spex_score_propall_std	0.26	0.09	3.00	0.003	*
epi_charccharc_non	0.02	0.13	0.13	0.894
epi_demo_howr_num	0.22	0.09	2.44	0.015	*
spex_score_propall_std:epi_charccharc_non	0.01	0.13	0.10	0.918
spex_score_propall_std:epi_demo_howr_num	-0.18	0.09	-2.04	0.042	*
epi_charccharc_non:epi_demo_howr_num	-0.17	0.13	-1.33	0.186
spex_score_propall_std:epi_charccharc_non:epi_demo_howr_num	0.23	0.12	1.95	0.052
Thailand
(Intercept)	-0.06	0.06	-1.04	0.298
spex_score_propall_std	0.39	0.07	5.27	<0.001	*
epi_charccharc_non	-0.35	0.83	-0.42	0.675
epi_demo_howr_num	0.33	0.06	5.31	<0.001	*
spex_score_propall_std:epi_charccharc_non	0.00	0.75	0.00	0.996
spex_score_propall_std:epi_demo_howr_num	0.03	0.06	0.59	0.554
epi_charccharc_non:epi_demo_howr_num	0.03	0.61	0.05	0.958
spex_score_propall_std:epi_charccharc_non:epi_demo_howr_num	0.54	0.49	1.10	0.275
China
(Intercept)	-0.37	0.06	-6.36	<0.001	*
spex_score_propall_std	0.22	0.06	3.55	<0.001	*
epi_charccharc_non	1.24	0.14	9.16	<0.001	*
epi_demo_howr_num	0.01	0.06	0.09	0.927
spex_score_propall_std:epi_charccharc_non	0.09	0.13	0.73	0.468
spex_score_propall_std:epi_demo_howr_num	-0.18	0.05	-3.20	0.002	*
epi_charccharc_non:epi_demo_howr_num	0.10	0.13	0.77	0.444
spex_score_propall_std:epi_charccharc_non:epi_demo_howr_num	0.28	0.12	2.28	0.024	*
Vanuatu
(Intercept)	-0.02	0.08	-0.29	0.774
spex_score_propall_std	0.43	0.08	5.43	<0.001	*
epi_charccharc_non	-0.03	0.16	-0.19	0.847
epi_demo_howr_num	-0.04	0.08	-0.50	0.616
spex_score_propall_std:epi_charccharc_non	0.07	0.15	0.51	0.611
spex_score_propall_std:epi_demo_howr_num	0.01	0.10	0.07	0.941
epi_charccharc_non:epi_demo_howr_num	0.29	0.18	1.60	0.112
spex_score_propall_std:epi_charccharc_non:epi_demo_howr_num	0.13	0.18	0.69	0.491

This analysis suggests that the relationship between Porosity and Spiritual Experience is signficantly positive among non-charismatics in all five sites considered individually, even after accounting for self-reported religiosity. The impact of reliosity on the strength of this relationship appears to vary across sites (though there’s only limited evidence of this from the previous analysis): it’s not significant in the US, Thai, or ni-Van samples alone, but significantly weaker among more religious participants in Ghana and China. I would take this with a big grain of salt.

Considering first five “beings” for all sites

Again, it is clear from the plot that there is a general relationship between Porosity and Spiritual Experience scores, and the strength of this relationships appears to vary somewhat across sites.

First, I’ll look at this relationship considering all sites in the same analysis (standardizing scores collapsing across all sites, to maintain the pattern fo general differences in Porosity and Spiritual Experience across sites):

Relationship between porosity and spiritual experience, comparing across sites [model: lm(por_score_std_collapse ~ spex_score_first5_std_collapse * epi_ctry, d_por_spex_base_q2to23_first5)]

parameter	b	standard error	t	p	significant
(Intercept)	0.03	0.02	1.32	0.189
spex_score_first5_std_collapse	0.39	0.02	18.85	<0.001	*
epi_ctrynonUS_US	0.10	0.01	11.93	<0.001	*
epi_ctryGHVT_THCH	0.65	0.02	27.02	<0.001	*
epi_ctryGH_VT	0.08	0.03	2.87	0.004	*
epi_ctryTH_CH	-0.06	0.04	-1.51	0.131
spex_score_first5_std_collapse:epi_ctrynonUS_US	-0.03	0.01	-3.05	0.002	*
spex_score_first5_std_collapse:epi_ctryGHVT_THCH	-0.13	0.02	-5.66	<0.001	*
spex_score_first5_std_collapse:epi_ctryGH_VT	-0.06	0.03	-2.34	0.02	*
spex_score_first5_std_collapse:epi_ctryTH_CH	-0.11	0.04	-2.72	0.007	*

This analysis is virtually identical to the parallel analysis using proportion scores, above—with the additional suggestion that the relationship was significantly weaker among participants in Thailand than participants in China (see “spex_score_first5_std:epi_ctryTH_CH” row in table above).

Next, I’ll look at this relationship considering each sites individually in its own analysis (standardizing scores within each site):

Relationship between porosity and spiritual experience, within each site separately [model: lm(por_score_std ~ spex_score_first5_std, d_por_spex_base_q2to23_first5 %>% filter(epi_ctry == ...)]

parameter	b	standard error	t	p	significant
US
(Intercept)	0.00	0.05	0.00	1
spex_score_first5_std	0.62	0.05	11.91	<0.001	*
Ghana
(Intercept)	0.00	0.06	0.00	1
spex_score_first5_std	0.30	0.06	4.88	<0.001	*
Thailand
(Intercept)	0.00	0.06	0.00	1
spex_score_first5_std	0.44	0.06	7.02	<0.001	*
China
(Intercept)	0.00	0.06	0.00	1
spex_score_first5_std	0.69	0.06	12.02	<0.001	*
Vanuatu
(Intercept)	0.00	0.06	0.00	1
spex_score_first5_std	0.49	0.06	7.82	<0.001	*

As with the analysis of proportion scores, this analysis suggests that the relationship between Porosity and Spiritual Experience is signficantly positive in all five sites considered individually, and the regression coefficients (“b” column in the table above) further confirm that this relationhip was strongest in China and the US, middling in Vanuatu and Thailand, and weakest (but still significantly positive) in Ghana.

Since this analysis was so similar to the analysis considering all beings, I will not bother breaking things down by charismatic status at this point.

Spiritual experience follow-up questions

Frequency questions (“How often…?”)

Questions #2-10

Questions #11-15 (+ more for Thailand)

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